class_docstr: Bridge class to connect langchain.embeddings module to mindsDB
class_docstr_tok: ["Bridge","class","to","connect","langchain.embeddings","module","to","mindsDB"]
code: def create(
      self,
      target: str,
      df: Union[DataFrame, None] = None,
      args: Union[Dict, None] = None,
  ) -> None:
      # get the class name from the args
      user_args = args.get("using", {})
  
      # infer the input columns arg if user did not provide it
      # from the columns of the input dataframe if it is provided
      if "input_columns" not in user_args and df is not None:
          # ignore private columns starts with __mindsdb
          # ignore target column in the input dataframe
          user_args["input_columns"] = [
              col
              for col in df.columns.tolist()
              if not col.startswith("__mindsdb") and col != target
          ]
          # unquote the column names -- removing surrounding `
          user_args["input_columns"] = [
              col.strip("`") for col in user_args["input_columns"]
          ]
  
      elif "input_columns" not in user_args:
          # set as empty list if the input_columns is not provided
          user_args["input_columns"] = []
  
      # this may raise an exception if
      # the arguments are not sufficient to create such as class
      # due to e.g., lack of API key
      # But the validation logic is handled by langchain and pydantic
      construct_model_from_args(user_args)
  
      # save the model to the model storage
      target = target or "embeddings"
      user_args[
          "target"
      ] = target  # this is the name of the column to store the embeddings
      self.model_storage.json_set("args", user_args)
  
file_path: mindsdb/mindsdb/integrations/handlers/langchain_embedding_handler/langchain_embedding_handler.py
imports: import copy
  import importlib
  from typing import Dict, Union
  import pandas
  from langchain.embeddings.base import Embeddings
  from pandas import DataFrame
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.utilities import log
label_desc: Use image node_id 2 for calling the LangchainEmbeddingHandler obj's underlying member method code with example usage: obj.create(target, df, args) without return types